Electronic toll management and vehicle identification

ABSTRACT

Identifying a vehicle in a toll system includes accessing image data for a first vehicle and obtaining first vehicle identifier data from the accessed image data for the first vehicle. A set of records is accessed. Each record includes first vehicle identifier data for a vehicle. The first vehicle identifier data for the first vehicle is compared with the first vehicle identifier data for vehicles in the set of records. Based on the results of the comparison of the first vehicle identifier data, a set of vehicles is identified from the vehicles having records in the set of records. Second vehicle identifier data is accessed for the first vehicle and is compared to second vehicle identifier data for the set of vehicles in order to identify the first vehicle.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation (and claims the benefit of priorityunder 35 USC 120) of U.S. application Ser. No. 13/608,510, filed on Sep.10, 2012, now allowed, which is a continuation of U.S. application Ser.No. 13/113,125, filed on May 23, 2011, now U.S. Pat. No. 8,265,988,issued Sep. 11, 2012, which is a continuation of U.S. application Ser.No. 11/423,683, filed on Jun. 12, 2006, now U.S. Pat. No. 7,970,644,issued Jun. 28, 2011, which claims priority to U.S. ProvisionalApplication Ser. No. 60/689,050, filed on Jun. 10, 2005 and which isalso a continuation-in-part of U.S. application Ser. No. 10/371,549,filed Feb. 21, 2003, now abandoned. The disclosures of the priorapplications are considered part of (and are incorporated by referencein) the disclosure of this application.

TECHNICAL FIELD

This document relates to electronic toll management.

BACKGROUND

Transportation facilities such as roads, bridges, and tunnels producetolls often representing a major source of income for many states andmunicipalities. The large number of automobiles, trucks, and busesstopping at tollbooths to pay a toll daily can cause significantproblems. For example, such facilities may restrict the flow of trafficcausing traffic backups and lane changing, often increasing thelikelihood of accidents and even more bottlenecks. In addition, manypeople may be delayed from reaching their destinations, and goods may bedelayed from getting to market and millions of gallons of fuel may bewasted as vehicles idle. Environments may experience an increase inpollution as idling and slow moving vehicles emit pollutants(particularly carbon dioxide and carbon monoxide), which may pose asignificant health hazard to motorists as well as to tollboothoperators.

Some tollbooth systems may have a program requiring that a motorist rentand then attach to the windshield of the vehicle a radio transponderthat communicates via radio frequency with receiver units at tollboothplazas. However, such programs require drivers to seek out the programand to register for the program. These programs may make it mandatoryfor a motorist to make a credit card deposit and create an automaticdebit account arrangement, which may effectively eliminate drivers withcredit problems. These programs also may bill participants based on aminimum amount of travel regardless of the actual amount of travel.Thus, many motorists who travel infrequently travel through the tollroad may receive little benefit after investing time and money toparticipate in the program.

SUMMARY

The present disclosure describes at least one toll system that enablesautomatic and electronic handling of payment of tolls by vehiclespassing a toll facility, without requiring the vehicles to slow down orto have a transponder. Such a system automatically identifies all orsubstantially all of the vehicles that pass the toll facility, and billsthe owner of each identified vehicle for the incurred toll fee.Unfortunately, due to the high number of vehicles that pass through atypical toll facility, known vehicle identification techniques (e.g.,license plate reading (LPR)) typically have too high of an error ratefor effective use in this system. For example, the error rate for atypical LPR system may be approximately 1%. While such an error rate maybe acceptable for toll systems that only identify vehicles that areviolators, this error rate is typically too high for a toll system thatattempts to identify every passing vehicle, not just the violators, forcollection of toll fees. In such a system, a 1% error rate can result ina significant loss of revenue (e.g., the loss of 1000 or more toll feesa day).

Additionally, typical LPR systems often exhibit a tradeoff between thenumber of vehicles identified (i.e., those vehicles for which the readresult exceeds a read confidence threshold for presumption of correctID) and the error rate. In an ideal world, this tradeoff would bereflected in a binary confidence continuum, where the system alwaysproduces a read confidence level of one when the read result is correctand a read confidence level of zero when the read result is incorrect.In reality, however, the read results are usually at least partiallycorrect, and the system generates a confidence continuum having a broadrange of confidence levels ranging, for example, from a level of one ornear one (very likely correct) to a level of zero or near zero (verylikely incorrect). The system, therefore, is often required to set anarbitrary read confidence threshold for determining which read resultswill be deemed correct. Once the read confidence threshold is set, anyread results having confidence levels above the threshold are deemedcorrect and any read results having confidence levels below thethreshold are deemed incorrect. Setting the read confidence thresholdtoo high (e.g., at 0.95 or higher) significantly decreases thepossibility of an error but also excludes many correct read results,thereby reducing revenue. Conversely, setting the read confidencethreshold too low (e.g., 0.3 or higher) increases the number of readsdeemed correct but also significantly increases the number of errors,thereby increasing costs by introducing errors into a large number ofaccounts/bills which require much time and effort to audit and correct.In a toll system that identifies every passing vehicle, this tradeoff isparticularly problematic since it may result in a significant loss ofprofits.

Moreover, a toll system that identifies every passing vehicle isidentifying a much larger number of vehicles than a conventional tollsystem, which typically only identifies violators. Accordingly, such atoll system attempts to identify every passing vehicle and is designedto both maximize revenue by identifying vehicles very accurately andlimit personnel costs by minimizing the need for manual identificationof vehicles and account/bill error processing.

In one particular implementation, to obtain a lower vehicleidentification error rate (and obtain a higher automated identificationrate), the toll system uses two vehicle identifiers to identify a targetvehicle. Specifically, the toll system collects image and/or sensor datafor the target vehicle and extracts two vehicle identifiers from thecollected data. The vehicle identifiers extracted from the collecteddata may include, for example, license plate information, a vehiclefingerprint, a laser signature, and an inductive signature for thetarget vehicle. In one particular implementation, the first vehicleidentifier is license plate information and the second vehicleidentifier is a vehicle fingerprint.

The toll system uses the first vehicle identifier to determine a set ofone or more matching vehicle candidates by searching a vehicle recorddatabase and including in the set only those vehicles associated withrecords having data that match or nearly match the first vehicleidentifier of the target vehicle. The toll system uses the secondvehicle identifier of the target vehicle to identify the target vehiclefrom among the set of matching vehicle candidates.

When the first vehicle identifier is license plate information and thesecond vehicle identifier is a vehicle fingerprint, the toll system mayeliminate the problematic trade-off between the number of vehiclesidentified and the error rate typical of LPR systems by using the LPRidentification for identification of the group of vehicle candidates,rather than for the final identification of the vehicle, and then usingthe much more accurate vehicle fingerprint matching for the finalidentification of the vehicle. Thus, incorrect reads by the LPR systemare eliminated during the final and more accurate fingerprint matchingidentification. This toll system may thereby be able to obtain extremelyaccurate identification results for a larger proportion of vehicles thanwould be obtained through license plate reading alone.

In particular, the toll system accesses the records of the matchingvehicle candidates and searches for one or more records that have datasufficiently similar to the second vehicle identifier of the targetvehicle so as to indicate a possible match. If no possible matches arefound for the target vehicle among the set of matching vehiclecandidates, the toll system may increase the size of the set by changingthe matching criteria and may once again attempt to identify one or morepossible matches for the target vehicle from among the larger set ofmatching vehicle candidates. If still no possible matches are found, thetoll system may enable a user to manually identify the target vehicle byproviding the user with access to the collected data for the targetvehicle and access to databases internal and/or external to the tollsystem.

If one or more possible matches are found, a confidence level isdetermined for each possible match. If the confidence level of apossible match surpasses an automated confidence threshold, the tollsystem automatically identifies the target vehicle without humanintervention as the vehicle corresponding to the possible match. If theconfidence level of a possible match surpasses a probable matchthreshold, the toll system presents the probable match to a humanoperator and enables the human operator to confirm or reject theprobable match. If no automatic match or confirmed probable match isfound, the toll system enables a user to manually identify the targetvehicle by providing the user with access to the collected data for thetarget vehicle and the possible matches identified by the toll system,and with access to databases internal and/or external to the tollsystem.

In this manner, the toll system typically obtains greater vehicleidentification accuracy by requiring that two vehicle identifiers besuccessfully matched for successful vehicle identification. Moreover,the identification process may be faster because the matching of thesecond identifier is limited to only those vehicle candidates havingrecords that successfully match the first vehicle identifier. Humanoperator intervention is also kept to a minimum through use of multipleconfidence level thresholds.

In one general aspect, identifying a vehicle in a toll system includesaccessing image data for a first vehicle and obtaining license platedata from the accessed image data for the first vehicle. A set ofrecords is accessed. Each record includes license plate data for avehicle. The license plate data for the first vehicle is compared withthe license plate data for vehicles in the set of records. Based on theresults of the comparison of the license plate data, a set of vehiclesis identified from the vehicles having records in the set of records.Vehicle fingerprint data is accessed for the first vehicle. The vehiclefingerprint data for the first vehicle is based on the image data forthe first vehicle. Vehicle fingerprint data for a vehicle in the set ofvehicles is accessed. Using a processing device, the vehicle fingerprintdata for the first vehicle is compared with the vehicle fingerprint datafor the vehicle in the set of vehicles. The vehicle in the set ofvehicles is identified as the first vehicle based on results of thecomparison of vehicle fingerprint data.

Implementations may include one or more of the following features. Forexample, comparing license plate data for the first vehicle with licenseplate data for vehicles in the set of records may include searching avehicle record database for records that include license plate data thatexactly match the license plate data obtained for the first vehicle.Comparing license plate data for the first vehicle may further includeperforming an extended search of the vehicle record database for recordsthat include license plate data that nearly match the license plate dataobtained for the first vehicle. The extended search may be conditionedon no vehicle identification records being found that include licenseplate data that exactly match the license plate data obtained for thefirst vehicle.

Comparing the license plate data for the first vehicle with the licenseplate data for vehicles in the set of records may include comparing thelicense plate data using predetermined matching criteria. Thepredetermined matching criteria may be changed to increase the number ofvehicles in the identified set of vehicles. Changing the predeterminedmatching criteria to increase the number of vehicles in the identifiedset of vehicles may be conditioned on a failure to identify any vehiclesin the set of vehicles as the first vehicle based on results of thecomparison of vehicle fingerprint data.

Identifying a vehicle in a toll system may further include capturinglaser signature data or inductive signature data for the first vehicle.The laser signature data may include data obtained by using a laser toscan the first vehicle. The laser signature data may include one or moreof an overhead electronic profile of the first vehicle, an axle count ofthe first vehicle, and a 3D image of the first vehicle.

The inductive signature data may include data obtained through use of aloop array over which the first vehicle passes. The inductive signaturedata may include one or more of an axle count of the first vehicle, atype of engine of the first vehicle, and a vehicle type or class for thefirst vehicle.

Each record in the set of records includes laser signature data orinductive signature data for a vehicle. Identifying a vehicle in a tollsystem may further include comparing laser signature data or inductivesignature data for the first vehicle with laser signature data orinductive signature data for vehicles in the set of records. Identifyinga set of vehicles from the vehicles having records in the set of recordsmay include identifying the set of vehicles based on the results of thecomparison of the license plate data and the results of the comparisonof the laser signature data or the inductive signature data.

Identifying the set of vehicles based on the results of the comparisonof license plate data and the results of the comparison of the lasersignature data or inductive signature data may include determining acombined equivalent matching score for each vehicle having a record inthe set of records and identifying the set of vehicles as a set ofvehicles having combined equivalent matching scores above apredetermined threshold. Each combined equivalent matching score mayinclude a weighted combination of a laser or inductive signaturematching score and a license plate matching score.

Identifying the vehicle in the set of vehicles as the first vehicle mayinclude identifying the vehicle as the first vehicle based on theresults of the comparison of the vehicle fingerprint data and theresults of the comparison of the laser signature data or inductivesignature data. Identifying the vehicle in the set of vehicles as thefirst vehicle based on the results of the comparison of the vehiclefingerprint data and the results of the comparison of the lasersignature data or inductive signature data may include determining acombined equivalent matching score for the vehicle in the set ofvehicles and determining that the combined equivalent matching score isabove a predetermined threshold. The combined equivalent matching scoremay include a weighted combination of a laser or inductive signaturematching score and a vehicle fingerprint matching score.

Identifying the vehicle in the set of vehicles as the first vehicle mayinclude identifying the vehicle as the first vehicle if the comparisonof the vehicle fingerprint data for the first vehicle with the vehiclefingerprint data for the vehicle in the set of vehicles indicates amatch having a confidence level that exceeds a confidence threshold.Identifying the vehicle in the set of vehicles as the first vehicle mayinclude identifying the vehicle as the first vehicle without humanintervention if the confidence level of the match exceeds a firstconfidence threshold and/or may include identifying the vehicle as thefirst vehicle if the confidence level of the match is less than thefirst confidence level but greater than a second confidence thresholdand a human operator confirms the match. The human operator may confirmor reject the match by enabling the operator to perceive the image datafor the first vehicle and enabling the human operator to interact with auser interface to indicate rejection or confirmation of the match.

Identifying the vehicle in the set of vehicles as the first vehicle mayinclude identifying the vehicle as the first vehicle if the confidencelevel of the match is less than the first and second confidencethresholds and a human operator manually identifies the vehicle as thefirst vehicle by accessing the image data for the first vehicle and therecord for the vehicle in the set of records. The human operator maymanually identify the vehicle in the set of vehicles as the firstvehicle by enabling the human operator to access the image data for thefirst vehicle, enabling the human operator to access the record for thevehicle in the set of records, and enabling the human operator tointeract with a user interface to indicate positive identification ofthe first vehicle as the vehicle in the set of vehicles. The humanoperator may be enabled to manually identify the vehicle in the set ofvehicles as the first vehicle by enabling the human operator to accessdata stored in databases of external systems.

Identifying the vehicle in the set of vehicles as the first vehicle mayinclude identifying the vehicle by combining vehicle identificationnumber (VIN), laser signature, inductive signature, and image data.

In another general aspect, an apparatus for identifying a vehicle in atoll system includes an image capture device configured to capture imagedata for a first vehicle. The apparatus further includes one or moreprocessing devices communicatively coupled to each other and to theimage capture device. The one or more processing devices are configuredto obtain license plate data from the captured image data for the firstvehicle and access a set of records. Each record in the set of recordsincludes license plate data for a vehicle. The one or more processingdevices are further configured to compare the license plate data for thefirst vehicle with the license plate data for vehicles in the set ofrecords and identify a set of vehicles from the vehicles having recordsin the set of records. The set of vehicles is identified based onresults of the comparison of the license plate data. The one or moreprocessing devices are further configured to access vehicle fingerprintdata for the first vehicle. The vehicle fingerprint data for the firstvehicle is based on the captured image data for the first vehicle. Theone or more processing devices are also configured to access vehiclefingerprint data for a vehicle in the set of vehicles, compare thevehicle fingerprint data for the first vehicle with the vehiclefingerprint data for the vehicle in the set of vehicles, and identifythe vehicle in the set of vehicles as the first vehicle based on resultsof the comparison of vehicle fingerprint data.

In another general aspect, identifying a vehicle in a toll systemincludes accessing image or sensor data for a target vehicle andextracting a first identifier and a second identifier from the image orsensor data. The extracted first identifier is used to identify a set ofone or more vehicle candidates as potential matches for the targetvehicle. The extracted second identifier is used to identify the targetvehicle as a vehicle selected from the set of one or more vehiclecandidates.

The above and other implementations and features are described in detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an implementation of an electronic tollmanagement system.

FIG. 2 is a flow chart of an implementation of an electronic tollmanagement system related to highlighted vehicle identifier management.

FIG. 3 is a flow chart of an implementation of an electronic tollmanagement system related to payment management.

FIG. 4 is a flow chart of an implementation of an electronic tollmanagement system related to payment management.

FIG. 5 is a flow chart of an implementation of an electronic tollmanagement system related to mailing address verification.

FIG. 6 is a block diagram of an implementation of an electronic tollmanagement system.

FIG. 7 is a flow chart of an implementation of an electronic tollmanagement system related to vehicle identification.

FIG. 8. is a flow chart of an implementation of an electronic tollmanagement system related to vehicle identification.

FIGS. 9A-9C are a flow chart of an implementation of an electronic tollmanagement system related to vehicle identification.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of an implementation of an electronic tollmanagement system 10. The system 10 is configured to capture a vehicleidentifier 31 of vehicle 30 interacting with a facility 28 and to notifyexternal systems 34 of such interaction. For example, the system 10 mayallow a toll road authority to capture a vehicle identifier 31, such aslicense plate information, from a vehicle 30 traveling through the tollroad and then to notify law enforcement whether the captured vehicleidentifier matches a license plate previously highlighted by lawenforcement.

The toll management system 10 also can manage payment from a partyassociated with the vehicle 32 based on the interaction between thevehicle 30 and the facility 28. For example, the system 10 can capturelicense plate information from a vehicle 30 and identify the registeredowner of the vehicle. The system would then provide to the owner, over acommunications channel such as the Internet, an account for makingpayment or disputing payment. The toll management system 10 can send abill requesting payment from the party 32 using a mailing address thathas been verified against one or more mailing address sources. Thesystem 10 is capable of automatically capturing an image of the vehicle30 triggered by the vehicle interacting with the facility. Such imagecapturing can be accomplished using image-processing technology withouthaving to install a radio transponder (e.g., RFID device) in a vehicle.

The electronic toll management system 10 includes a toll managementcomputer 12 which can be configured in a distributed or a centralizedmanner. Although one computer 12 is shown, one or more computers can beconfigured to implement the disclosed techniques. The computer 12 iscoupled to a facility 28 that may charge a fee for interacting with thefacility. Examples of a facility 28 include a toll facility (managed bytoll authorities) such as toll road, a toll bridge, a tunnel, parkingfacility, or other facility. The fee may be based on the interactionbetween the vehicle 30 and the facility 28. Examples of interactionsthat may involve a fee include a distance traveled by the vehiclethrough the facility, a time period the vehicle is present in afacility, the type of vehicle interacting with the facility, the speedat which the vehicle passes through the facility, and the type ofinteraction between the vehicle and the facility.

The facility 28 can process vehicles including automobiles, a truck,buses, or other vehicles. For ease of explanation, the system 10 shows asingle facility 28 interacting with a single vehicle 30 and a partyassociated with the vehicle 32. However, in other implementations, thedisclosed techniques could be configured to operate with one or morevehicles interacting with one or more facilities spanning differentgeographic locations.

The toll management computer 12 includes an image acquisition module 24configured to detect the presence of a vehicle, acquire one or moreimages of the vehicle, and forward the image(s) to an image-processingmodule 25 for further processing. The module 24 may include imageacquisition equipment based on the physical environment in which it isused.

For example, for open-road applications, image acquisition equipment maybe mounted above the roadway, on existing structures or on purpose-builtgantries. Some open-road applications may use equipment mounted in orbeside the roadway as well. Lane-based (or tollbooth-style) applicationsmay use equipment mounted on physical structures beside each lane,instead of or in addition to equipment mounted overhead or in theroadway.

The image acquisition module 24 may include imaging components such asvehicle sensors, cameras, digitizing systems, or other components.Vehicle sensors can detect the presence of a vehicle and provide asignal that triggers a camera to capture one or more images of thevehicle. Vehicle sensors may include one or more of the following:

(1) Laser/sonic/microwave devices—these devices, commonly used inIntelligent Transportation Systems (ITS) applications, can recognize thepresence of a vehicle and provide information regarding the vehicle'ssize, classification, and/or speed. These sensors may be configured toprovide additional information about the vehicle which can be used inidentify the vehicle and its use of the toll facility, including triptime and compliance with traffic laws.

(2) Loops—these sensors can detect the presence and the vehicle type byrecognizing the presence of metal masses using a wire loop embedded inthe road. Loops can be used as a backup to more sophisticated sensors.Loops can also be used as a primary source of data to detect vehicles,classify vehicles, trigger cameras, and provide vehicle signature data(e.g., based on use of an array of loops with a smart loop controlprogram such as Diamond Consulting's IDRIS® system of Buckinghamshire,United Kingdom).

(3) Through-beam sensors—these sensors may emit a continuous beam acrossthe roadway, and detect the presence of a vehicle based uponinterruptions in the beam. This type of sensor may be used ininstallations where traffic is channeled into tollbooth-style lanes.

(4) Optical sensors—vehicle may be recognized using cameras tocontinuously monitor images of the roadway for changes indicating thepresence of a vehicle. These cameras also can be used to record imagesfor vehicle identification.

Cameras can be used to capture images of vehicles and their identifyingcharacteristics. For example, they can be used to generate a vehicleidentifier such as a vehicle license number based on an image of alicense plate. Cameras may be analog or digital, and may capture one ormore images of each vehicle.

Digitizing systems convert images into digital form. If analog camerasare used, the cameras can be connected to separate digitizing hardware.This hardware may include a dedicated processing device foranalog-to-digital conversion or may be based on an input deviceinstalled in a general-purpose computer, which may perform additionalfunctions such as image processing. Lighting can be employed to provideadequate and consistent conditions for image acquisition. The lightingmay include strobes or continuous illumination, and may emit light oflight in the visible spectrum or in the infrared spectrum. If strobesare used, they may be triggered by inputs from the vehicle sensor(s).Other sensors such as light sensors may be required to control the imageacquisition module 24 and provide consistent results.

Once the image acquisition module 24 has captured images of thevehicles, the images may be forwarded to an image-processing module 25.The image-processing module 25 may be located in the same location asthe image acquisition module 24 and the image computer 12, in a remotelocation, or a combination of these locations. The module 25 can processa single image for each vehicle or multiple images of each vehicle,depending on the functionality of the image acquisition module 24 and/orbusiness requirements (e.g., accuracy, jurisdictional requirements). Ifmultiple images are used, each image may be processed, and the resultsmay be compared or combined to enhance the accuracy of the process. Forexample, more than one image of a rear license plate, or images of bothfront and rear license plates, may be processed and the results comparedto determine the most likely registration number and/or confidencelevel. Image processing may include identifying the distinguishingfeatures of a vehicle (e.g., the license plate of a vehicle) within theimage, and analyzing those features. Analysis may include opticalcharacter recognition (OCR), template matching, or other analysistechniques.

The toll management system 10 may include other systems capable ofsubstantially real-time processing located at the site where images areacquired to reduce data communication requirements. In an implementationof local image processing, the results may be compared to a list ofauthorized vehicles. If a vehicle is recognized as authorized, imagesand/or data may be discarded rather than forwarded for furtherprocessing.

Images and data can be forwarded to a central processing facility suchas the image database 14 operating in conjunction with the billingengine 22. This process may involve a computer network, but may alsoinclude physical media from another computer located at the imageacquisition site (i.e., facility 28). Generally, information can betemporarily stored on a computer at the image acquisition site in theevent the network is unavailable.

Images received at the central site may not have been processed. Anyunprocessed images can be handled as described above. The data resultingfrom image processing (remote or central) may be separated into twocategories. Data that meets application-specific orjurisdiction-specific criteria for confidence may be sent directly tothe billing engine 22. On the other hand, data results not meetingrequired confidence levels may be flagged for additional processing.Additional processing may include, for example, determining whethermultiple images of a vehicle are available and independently processingthe images and comparing the results. This may includecharacter-by-character comparisons of the results of optical characterrecognition (OCR) on the license plate image. In another example, theimage(s) may be processed by one or more specialized algorithms forrecognizing license plates of certain types or styles (such as platesfrom a particular jurisdiction). These algorithms may consider thevalidity of characters for each position on the license plate, theanticipated effect of certain design features (such as backgroundimages), or other style-specific criteria. The processed image may beforwarded based on preliminary processing results, or may includeprocessing by all available algorithms to determine the highestconfidence level.

Preliminary data may be compared to other data available to increase theconfidence level. Such techniques include:

(1) Comparing OCR processed license plate data against lists of validlicense plate numbers within the billing system or at the appropriatejurisdiction's motor vehicle registration authority.

(2) Comparing other data obtained from sensors at the imaging location(such as vehicle size) to known characteristics of the vehicleregistered under the registration number recognized by the system, inthe recognized jurisdiction or in multiple jurisdictions.

(3) Comparing the registration and other data to records from othersites (e.g., records of the same or similar vehicle using otherfacilities on the same day, or using the same facility at other times).

(4) Comparing vehicle fingerprint data against stored lists of vehiclefingerprint data. The use of vehicle fingerprint data for vehicleidentification is described in more detail below.

(5) Manually viewing the images or data to confirm or override theresults of automated processing.

If additional processing provides a result with a particular confidencelevel, the resulting data then can be forwarded to the billing engine22. If the required confidence level cannot be attained, the data may bekept for future reference or discarded.

The billing engine 22 processes the information captured during theinteraction between the vehicle and the toll facility, including thevehicle identifier as determined by the image processing module 25 tocreate a transaction event corresponding to an interaction between thevehicle and the facility. The engine 22 can store the transaction eventin a billing database 16 for subsequent payment processing. For example,the billing engine 22, alone or in combination with a customermanagement module 26 (described below), produces payment requests basedon the transaction events. The transaction event data may includeindividual charges based on a vehicle's presence at specific points orfacilities, or trip charges based on a vehicle's origin and destinationinvolving a facility. These transaction events can be compiled andbilled, for example, by one or more of the following methods:

(1) Deducting payment from an account established by the vehicle owneror operator. For example, the billing database 20 can be used to storean account record for each vehicle owner. In turn, each account recordcan include a reference to one more transaction events. A paper orelectronic payment statement may be issued and sent to the registeredowner of the vehicle.

(2) Generating a paper bill and sending it to the owner of the vehicleusing a mailing address derived from a vehicle registration record.

(3) Presenting an electronic bill to a predefined account for thevehicle owner, hosted either by the computer 12 or a third party.

(4) Submitting a bill to the appropriate vehicle registration authorityor tax authority, permitting payment to be collected during the vehicleregistration renewal process or during the tax collection process.

Billing may occur at regular intervals, or when transactions meet acertain threshold, such as maximum interval of time or maximum dollaramount of outstanding toll charges and other fees. Owners may be able toaggregate billing for multiple vehicles by establishing an account withthe computer 12.

The customer management module 26 can allow a user to interact with thetoll management computer 12 over a communications channel such as acomputer network (e.g., Internet, wired, wireless, etc.), a telephoneconnection, or other channel. The user can include a party associatedwith a vehicle 22 (e.g., owner of the vehicle), a public or privateauthority responsible for management of the facility 28, or other user.The customer management module 26 includes a combination of hardware andsoftware module configured to handle customer interactions such as anaccount management module 26 a, a dispute management module 26 b and apayment processing module 26 c. The module 26 employs secure accesstechniques such as encryption, firewalls, password or other techniques.

The account management module 26 a allows users such as motorists tocreate an account with the system 10, associate multiple vehicles withthat account, view transactions for the account, view images associatedwith those transactions, and make payments on the account. In oneimplementation, a user responsible for the facility can access billingand collection information associated with motorists that have used thefacility.

The dispute management module 26 b may permit customers to disputespecific transactions on their accounts and to resolve disputes usingthe computer 12 or third parties. Disputes may arise during billingsituations. The module 26 b may help resolve such disputes in anautomated fashion. The module 26 b can provide a customer to access an“eResolution” section of a controlling/billing authority website.Customers can file a dispute and download an image of their transaction,the one in question. If there is no match (i.e., the customersautomobile is not the automobile in the photo frame), the bill can beforwarded for a third party evaluation such as arbitration. In the farmore likely case, the photo will show that the customer's automobile wasindeed billed correctly. Dispute management can use encrypted securityin which all text and images are sent over a computer network (e.g., theInternet) using high strength encryption. Proof of presence images canbe embedded into the dispute resolution communication as an electronicwatermark.

The payment processing module 26 c provides functionality for processingpayments manually or electronically, depending on the remittancereceived. For example, if payment remittance is in the form of a papercheck, then scanning devices could be used to convert the paperinformation into electronic format for further processing. On the otherhand if electronic payment is employed, then standard electronic paymenttechniques can be used. The payment processing module 26 c can supportbilling methods such as traditional mailing, electronic payment (e.g.using a credit card, debit card, smart card, or Automated Clearing Housetransaction), periodic billing (e.g., send the bill monthly, quarterly,upon reaching a threshold, or other). The payment processing module 26 ccan support discounts and surcharges based on frequency of usage, methodof payment, or time of facility usage. The payment processing module 26c also can support payment collection methods such as traditional checkprocessing, processing payment during renewal of a vehicle registration(with interest accrued), electronic payment, direct debit bank, creditcards, pre-payment, customer-initiated payments (as often as thecustomer desires), or provide discounts for different purposes.

The toll management computer 12 communicates with external systems 34using one or more communications techniques compatible with thecommunications interfaces of the systems. For example, communicationsinterfaces can include computer networks such as the Internet,electronic data interchange (EDI), batch data file transfers, messagingsystems, or other interfaces. In one implementation, external systems 34include law enforcement agencies 36, postal authorities 38, vehicleregistration authorities 40, insurance companies 42, service providers44, financial systems 46 and a homeland security agency 48. The externalsystems 34 can involve private or public organizations that span one ormore geographic locations such as states, regions, countries, or othergeographic locations.

The toll management computer 12 can interface and exchange informationwith law enforcement agencies 36. For example, as vehicles areidentified, the computer can submit substantially real-time transactionsto law enforcement systems, in formats defined by the law enforcementagencies. Transactions also can be submitted for vehicles carryinghazardous materials or violating traffic regulations (e.g. speeding,weight violations, missing plates), if the appropriate sensors are inplace (e.g. laser/sonic/microwave detectors as described above, weightsensors, radiation detectors). Alternatively, vehicle records can becompiled and forwarded in batches, based on lists provided by lawenforcement agencies.

The highlighted vehicle identifier database 20 can be used to store thelists provided by the law enforcement agencies. The term “highlighted”refers to the notion that the law enforcement agencies have provided alist of vehicle identifiers that the agencies have indicated(highlighted) they wish the toll facility to monitor. For example, whena motor vehicle is stolen and reported to police, the police can send alist of highlighted vehicle identifiers to the database 20. When thevehicle highlighted by the police travels through facility, the imagingprocessing module 24 determines a vehicle identifier associated with thevehicle and determines through certain interfaces that the particularvehicle is being sought by law enforcement. The law enforcementauthorities may wish to be instantly notified of the location of thevehicle (and driver), the time it was detected at the location, and thedirection it was headed. The computer 12 can notify in substantiallyreal-time mobile units associated with law enforcement. In addition, lawenforcement can automatically highlight vehicles based upon theexpiration of a license, occurrence of a traffic court date, or otherevent. This could, in turn, help keep illegal drivers off the road andincrease revenue to the state.

The toll management computer 12 can interface and exchange informationwith postal authorities 38. Since the disclosed techniques would requiretoll authorities to convert from receiving payment by drivers at thetime of travel to receiving paying in arrears, it is important thatbills be sent to the correct driver/vehicle owner. To minimize thepossibility of sending the bill to the wrong person, the computer 12supports address reconciliation. For example, before a bill is mailed,the computer 12 verifies that the address provided by a motor vehicledepartment matches the address provided by the postal authority. Themotor vehicle database can then be updated with the most accurateaddress information related to the vehicle owner. Since this occursbefore the bill is mailed, billing errors can be reduced.

The toll management computer 12 can interface and exchange informationwith vehicle registration authorities 40. The registration authorities40 provide an interface to exchange information related to the owners ofvehicles, the owners' addresses, characteristics of the vehicles, orother information. Alternatively, this information can be accessedthrough third-party data providers rather than through an interface topublic motor vehicle records. The accuracy of records in the variousdatabases used by the computer 12, including vehicle ownership and owneraddresses, may be verified periodically against third-party databases orgovernment records, including motor vehicle records and address records.This may help ensure the quality of ownership and address records, andreduce billing errors and returned correspondence.

The toll management computer 12 can interface and exchange informationwith insurance companies 42. Insurance companies could highlight vehicleidentifiers in a manner similar to law enforcement authorities 36. Forexample, the highlighted vehicle identifiers database 20 can includelicense plate numbers of vehicles with an expired insurance indicatingthat such drives would be driving illegally. The computer could notifylaw enforcement as well as insurance companies whether the highlightedvehicle has been detected using a particular facility.

The toll management computer 12 can interface and exchange serviceproviders 44. For example, the computer 12 can support batch orreal-time interfaces for forwarding billing and payment collectionfunctions to billing service providers or collection agencies.

The toll management computer 12 can interface and exchange informationwith financial systems 46. For example, to handle bill payment andcollection, the computer 12 can interface to credit card processors,banks, and third-party electronic bill presentment systems. The computer12 can also exchange information with accounting systems.

The toll management computer 12 can interface and exchange informationwith the homeland security agency 48. The office of homeland securitycan automatically provide a list of individuals for use in thehighlighted vehicle identifier database 20. For example, registereddrivers that are on a visa to this country can be automaticallyhighlighted when that visa expires. The computer 12 would then notifythe office of homeland security 48 that the highlighted vehicleidentifier associated with the person has been detected driving in thecountry including the time and location information about the vehicle.

As described above, data captured from the toll site flows into theimage database, and is retrieved from the image database by the billingengine. In another implementation, the toll computer detects, for eachvehicle, an interaction between the vehicle and a toll facility,captures images and generates a data record. The data record can includedate, time, and location of transaction, a reference to the image file,and any other data available from the sensors at the facility (e.g.,speed, size). The image can be passed to the image-processing module 25,which can generate a vehicle identifier, a state, and a confidencefactor for each vehicle.

This information can be added to the data record. (This process my occurafter transmission to the central facility.) The data record and imagefile can be sent to the central facility. The image can be stored in theimage database, and referenced if (a) additional processing is requiredto identify the vehicle, or (b) someone wishes to verify thetransaction. If the confidence level is adequate, the data record can besubmitted to the billing engine, which can associate it with an accountand store it in the billing database for later billing. If no accountexists, the vehicle identifier is submitted to the appropriate stateregistration authority or a third-party service provider to determinethe owner and establish an account. This process may be delayed untilenough transactions are collected for the vehicle to justify issuing abill. If confidence level is not adequate, additional processing may beperformed as described elsewhere.

The techniques described above describe the flow of data based on asingle transaction end-to-end, then looping back to the beginning. Inanother implementation, some of the functions described may beevent-driven or scheduled, and may operate independently of one another.For example, there may be no flow of control from back-end processes tovehicle imaging. The imaging process may be initiated by an event,including the presence of a vehicle at the toll site.

In another implementation, the system may be used to monitor traffic andmanage incidents. For example, if a drop in average vehicle speed isdetected, the computer can send a message to a highway control facilityalerting controllers to the possibility of an incident. Authorizedcontrollers may communicate with the equipment at the toll site to viewimages from the cameras and determine if a response is required.

The operation of the toll management system 10 is explained withreference to FIGS. 2-5.

FIG. 2 is a flow chart of an implementation of electronic tollmanagement system related, particularly a process 100 for managinghighlighted vehicle identifiers 20 provided by external systems 34. Toillustrate, in one example, it is assumed that law enforcement agencies36 generate a list of highlighted vehicle identifiers (e.g., licenseplate numbers) of drivers being sought by the agencies and that theagencies 36 wish to be notified when such vehicles have been identifiedusing a toll facility 28.

The computer 12 obtains (block 102) highlighted vehicle identifiers froma party such as law enforcement agencies 36. In one implementation,these vehicle identifiers can be stored in the vehicle identifierdatabase 20 for subsequent processing. The database 20 can be updated bythe agencies with new as well as additional information in real-timeand/or in batch mode. The law enforcement agencies accessed by thecomputer span across multiple jurisdictions such as cities,municipalities, states, regions, countries or other geographicdesignations. As a result, the computer 12 can process vehicleinformation across multiple jurisdictions and on a national scale.

The computer 12 captures (block 104) an image of a vehicle triggered bya transaction event based on an interaction between the vehicle 30 andthe facility 28. For example, the image acquisition module 24 can beused to acquire one or more images of a vehicle as it travels through afacility such as a toll road. These images can be stored in the imagedatabase 14 for further processing by the image-processing module 25.Compression techniques can be applied to the captured images to helpreduce the size of the database 14.

The computer 12 determines (block 106) a vehicle identifier based on thecaptured image. For example, as discussed previously, theimage-processing module 25 can apply image analysis techniques to theraw images in the image database 14. These analysis techniques canextract a license number from one or more images of a license plate ofthe vehicle. The extracted vehicle identifiers can be stored in thevehicle identifier database 18 for further processing.

The computer 12 compares (block 108) a captured vehicle identifier withthe highlighted vehicle identifier. For example, the computer 12 cancompare a captured license plate number from the vehicle identifierdatabase 18 with a license number from the highlighted vehicleidentifier database 20. As discussed above, automatic as well as manualtechniques can be applied to check for a match.

If the computer 12 detects a match (block 110) between the licensenumbers, then it checks (block 112) how the party associated with thehighlighted vehicle identifiers wishes to be notified. This informationcan be stored in the vehicle identifier database 20 or other storagemechanism. On the other hand, if there is no match, the computer 12resumes executing the process 100 beginning at block 102.

If the party indicates that it wishes to be notified immediately (block114), then the computer notifies (block 118) the party upon theoccurrence of a match. In this example, the computer can notify lawenforcement of the match in substantially real-time using wirelesscommunications techniques or over a computer network.

On the other hand, if the party does not wish to be notified immediately(block 114), then the computer 12 stores (block 116) the match for laternotification upon satisfaction of predefined criteria. In oneimplementation, predefined criteria can include gathering a predefinednumber of matches and then sending the matches to law enforcement inbatch mode.

Once the party has been notified (block 118) of a match or the match hasbeen stored for later notification (block 116), the computer 12 resumesexecuting process 100 beginning at block 102.

FIG. 3 is a flow chart of an implementation of electronic tollmanagement system 10, particularly a process 200 for managing paymentfrom a party associated with a vehicle that has interacted with afacility. To illustrate, in one example, it is assumed that a toll roadauthority decides to employ the disclosed techniques to handle paymentprocessing including billing and collecting tolls from vehicles usingits toll road.

The computer 12 captures (block 202) an image of a vehicle triggered bya transaction event based on an interaction between the vehicle and afacility. This function is similar to the process discussed above inreference to block 104 of FIG. 2. For example, the image acquisitionmodule 24 can be used to acquire one or more images of a vehicle 30 asit travels through the toll road 28. These images can be stored in theimage database 14 for further processing by the image-processing module25.

The computer 12 determines (block 204) a vehicle identifier based on thecaptured image. This function is also similar to the process discussedabove in reference to block 106 of FIG. 2. For example, theimage-processing module 25 can be used to extract a license number fromone or more images of a license plate of the vehicle. These vehicleidentifiers can be stored in the vehicle identifier database 18 forfurther processing.

The computer 12 determines (block 206) a party associated with thevehicle identifier by searching a registration authority databases. Forexample, the computer 12 can use the vehicle identifier from the vehicleidentifier database 18 to search a database of a vehicle registrationauthority 40 to determine the registered owner of the vehicle associatedwith the vehicle identifier. The computer 12 is capable of accessingvehicle information from one or more vehicle registration databasesacross multiple jurisdictions such as cities, municipalities, states,regions, countries or other geographic locations. In one implementation,the computer 12 can maintain a copy of registration information frommultiple registration authorities for subsequent processing.Alternatively, the computer 12 can access multiple registrationauthorities and obtain registration information on a demand basis. Ineither case, these techniques allow the computer 12 to process vehicleinformation across multiple jurisdictions, and thus process vehicles ona national scale.

The computer 12 checks (block 208) whether to request payment from theparty associated with the vehicle identifier. The request for paymentcan depend on payment processing information associated with theregistered owner. For example, the registered owner may be sent a billbased on a periodic basis (e.g., monthly basis), when a predefinedamount has been reached, or other arrangement.

If the computer 12 determines that payment is required (block 210), thenit requests (block 214) payment from the party associated with thevehicle identifier based on the transaction event. As discussed above, arequest for payment can be generated using traditional mail servicetechniques or electronic techniques such as electronic payment. Theamount of the bill can depend on information from the transaction eventsuch as the nature of the interaction between the vehicle and thefacility. For example, the transaction event can indicate that thevehicle traveled a particular distance defined as a distance between astarting and ending point on the toll road. Accordingly, the amount ofthe payment requested from the registered owner can be based on thedistance traveled.

On the other hand, if the computer 12 determines that payment is notrequired (block 210), then it forwards (block 212) the transaction eventto another party to handle the payment request. For example, the tollauthority may have decided that the computer 12 can handle imageprocessing functions and that toll billing and collection should behandled by a third party such as external systems 34. In oneimplementation, the computer 12 can interface with service providers 44and financial systems 48 to handle all or part of the billing andpayment-processing functionality. Once the transaction event has beenforwarded to a third party, the computer 12 resumes executing thefunctions of process 200 beginning at block 202.

If the computer handles payment processing, the computer 12 processes(block 216) a payment response from the party associated with thevehicle identifier. In one implementation, the billing database 16, inconjunction with the billing engine 22 and the customer managementmodule 26, can be used to handle billing and collection functions. Asdiscussed above, the payment processing module 26 c can supportelectronic or manual payment processing depending on the remittancereceived. For example, the computer 12 can provide an account forhandling electronic payment processing over a computer network such asthe Internet. The computer can also handle traditional payment receiptsuch as a check.

Once a payment has been processed (block 216), the computer 12 resumesexecuting process 200 beginning at block 202.

FIG. 4 is a flow chart of an implementation of electronic tollmanagement system 10, particularly process 300 for managing payment overa communications channel from a party associated with a vehicle that hasinteracted with a facility. To illustrate, assume a toll authorityresponsible for a toll road employs the disclosed techniques and that aregistered owner wishes to efficiently and automatically make paymentsfor using the toll road.

The computer 12 provides (block 302) an account for a party associatedwith the vehicle identifier. In one embodiment, the computer 12 inconjunction with the account management module 26 a can provide awebsite for customers to open an account for making electronic paymentover a computer network such as the Internet. The website also canpermit the customer to access and update account information such aspayment history, payment amount due, preferred payment method, or otherinformation.

The computer 12 receives (block 304) a request over a communicationschannel from the party to review a transaction event. For example, theaccount payment module 26 a can handle this request by retrievingtransaction event information associated with the customer's accountfrom the billing database 16. The retrieved information can includeimage data of a particular transaction involving the customer's vehicleand the tollbooth.

The computer 12 sends (block 306) the transaction event to the party 32over the communications channel. Information related to the transactionevent can include images of the vehicle and the vehicle identifier(i.e., license plate). Such data can be encrypted to permit securetransmission over the Internet. Standard communications protocols suchas hypertext markup language (HTML) can be used to transmit theinformation over the Internet.

The computer 12 determines (block 308) whether the party agrees to makepayment. For example, once the customer receives the information relatedto the transaction event, the customer can review the information todetermine whether to make payment based on whether the vehicle shown inthe images is the customer's vehicle.

If the computer 12 determines (block 310) that the party agrees to pay,then it processes (block 314) payment from the party by deducting anamount from the account based on the transaction event. For example, ifthe image information indicates that the transaction event data isaccurate, then the customer can authorize payment such as by submittingan electronic payment transaction.

On the other hand, if the computer 12 determines (block 310) that theparty does not agrees to pay, then the computer 12 processes (block 312)a payment dispute request from the party. In one implementation, thedispute management module 26 b can handle a dispute request submitted bythe customer using online techniques. The module 26 b can handlespecific transactions related to the customer's account includinginvolving a third party to resolve the dispute.

Once a payment has been processed (block 314) or a dispute resolved(block 312), the computer 12 resumes executing process 300 beginning atblock 304.

FIG. 5 is a flow chart of an implementation of electronic tollmanagement system, particularly a process 400 for reconciling mailingaddresses from different sources. To illustrate, it is assumed that atoll authority has decided to employ the disclosed techniques forprocessing payment related to the use of toll facility. Since thedisclosed techniques involve processing payment some time after thevehicle has traveled through the toll authority, these techniques helpensure that payment is sent to the correct address of the registeredowner of the vehicle.

The computer 12 determines (block 402) that a payment request is to besent to a party associated with a vehicle identifier. As explainedabove, for example, payment requests may be generated based on aperiodic basis or on an amount threshold basis.

The computer 12 accesses (block 404) a vehicle registration authorityfor a mailing address of a party associated with the vehicle identifier.For example, the computer 12 may access one or more databases associatedwith vehicle registration authorities 40 to retrieve information such asthe mailing address of the registered owner of the vehicle.

The computer 12 accesses (block 406) a postal authority for a mailingaddress of the party associated with the vehicle identifier. Forexample, the computer 12 may access one or more databases associatedwith postal authorities 38 to retrieve information such as the mailingaddress of the registered owner of the vehicle.

The computer 12 compares (block 408) the mailing address from thevehicle registration authority with the mailing address from the postalauthority. For example, the computer compares the mailing addresses fromthe two authorities to determine if there is a discrepancy between thedatabase information.

If the computer 12 determines (block 410) that the addresses match, thenit requests (block 414) payment from the party associated with thevehicle identifier using the mailing address accessed from the postalauthority. For example, the computer 12 can use the techniques discussedabove to handle payment processing including billing and collectingpayment from the registered owner.

On the other hand, if the computer 12 determines (block 410) that theaddresses do not match, it then updates (block 412) the vehicleregistration authority with the mailing address from the postalauthority. For example, the computer 12 can update databases associatedwith vehicle registration authorities 40 with the correct mailingaddress retrieved from the postal authorities 38. Such techniques mayhelp reduce the likelihood of mailing a bill to an incorrect mailingaddress resulting in an reducing time for payment remittance.

Once the vehicle registration authority has been updated (block 412) orpayment requested (block 414), the computer 12 executes process 400beginning at block 402 as explained above.

FIG. 6 is a block diagram of an implementation of an electronic tollmanagement system 600 that provides vehicle identification by extractingmultiple vehicle identifiers for each vehicle that interacts with thetoll facility. The toll management system 600 includes a toll managementcomputer 612. The toll management computer includes an image database614, a billing database 616, a vehicle identification database 618, ahighlighted vehicle identifier database 620, a billing engine 622, animage acquisition module 624, an image processing module 625, and acustomer management module 626. The toll management computer 612communicates with or is integrated with a toll facility 628, whichinteracts with a vehicle 630 and a party associated with the vehicle632. The toll management computer 612 also communicates with externalsystems 634.

Examples of each element within the toll management system 600 of FIG. 6are described broadly above with respect to FIG. 1. In particular, thetoll management computer 612, the image database 614, the billingdatabase 616, the vehicle identification database 618, the highlightedvehicle identifier database 620, the billing engine 622, the imageacquisition module 624, the image processing module 625, the customermanagement module 626, and the toll facility 628 typically haveattributes comparable to and illustrate one possible implementation ofthe toll management computer 12, the image database 14, the billingdatabase 16, the vehicle identification database 18, the highlightedvehicle identifier database 20, the billing engine 22, the imageacquisition module 24, the image processing module 25, the customermanagement module 26, and the toll facility 28 of FIG. 1, respectively.Likewise, the vehicle 630, the party associated with the vehicle 632,and the external systems 634 typically have attributes comparable to thevehicle 30, the party associated with the vehicle 32, and the externalsystems 34 of FIG. 1.

The vehicle identification database 618 includes an extracted identifierdatabase 6181, a vehicle record database 6182, and a read errorsdatabase 6183. The functions of the databases 6181-6183 are described inmore detail below.

The system 600 is similar to system 10 and is configured to provide, forexample, reduced vehicle identification error rates by identifying eachvehicle through use of multiple vehicle identifiers. Two suchidentifiers are designated as 631A and 631B. A vehicle identifier ispreferably an identifier that uniquely or substantially uniquelyidentifies the vehicle but may be an identifier that helps in theidentification process by distinguishing the vehicle from other vehicleswithout necessarily uniquely identifying the vehicle. Identifiers 631Aand 631B may be part of vehicle 630, as suggested by FIG. 6, but neednot be. For example, identifiers 631A and/or 631B may be produced byimage processing module 625 based on characteristics of the vehicle 630.

As described previously, one example of a vehicle identifier is licenseplate information of a vehicle, such as a license plate number andstate. The image processing module 625 may determine the license plateinformation of a vehicle from an image of the license plate by usingOCR, template matching, and other analysis techniques. A license platenumber may include any character but is typically restricted toalphanumeric characters. License plate information typically may be usedto uniquely identify the vehicle.

Another example of a vehicle identifier is a vehicle detection tag asdescribed in U.S. Pat. No. 6,747,687, hereby incorporated by referencein its entirety for all purposes. The vehicle detection tag, hereinafterreferred to as a vehicle fingerprint, is a distilled set of dataartifacts that represent the visual signature of the vehicle. The imageprocessing module 625 may generate a vehicle fingerprint by processingan image of the vehicle. To save on processing time and storage needshowever, the generated vehicle fingerprint typically does not includethe normal “picture” information that a human would recognize.Accordingly, it is usually not possible process the vehicle fingerprintto obtain the original vehicle image. Some vehicle fingerprints,however, may include normal picture information. A vehicle fingerprinttypically may be used to uniquely identify the vehicle.

In one implementation, a camera in the image acquisition module 624captures a single “still” image of the back of each vehicle that passesthe toll facility 628. For each vehicle, the image processing module 625recognizes the visual cues that are unique to the vehicle and reducesthem into a vehicle fingerprint. Because a license plate is a veryunique feature, the image processing module 625 typically maximizes theuse of the license plate in creating the vehicle fingerprint. Notably,the vehicle fingerprint also includes other parts of the vehicle inaddition to the license plate and, therefore, vehicle identificationthrough matching of vehicle fingerprints is generally considered moreaccurate than vehicle identification through license plate informationmatching. The vehicle fingerprint may include, for example, portions ofthe vehicle around the license plate and/or parts of the bumper and thewheelbase.

Another example of a vehicle identifier is a vehicle signature generatedusing a laser scan (hereinafter referred to as a laser signature). Thelaser signature information that may be captured using a laser scan mayinclude one or more of an overhead electronic profile of the vehicle,including the length, width, and height of the vehicle, an axle count ofthe vehicle, and a 3D image of the vehicle. In one implementation, theimage acquisition module 624 includes two lasers for a given lane, onethat is mounted over the lane and another that is mounted alongside ofthe lane. The laser mounted above the lane typically scans the vehicleto capture the overhead profile of the vehicle, and the laser mountedalongside or above of the lane typically scans the vehicle to capturethe axle count of the vehicle. Together, both lasers are also able togenerate a 3D image of the vehicle. A laser signature may be used touniquely identify some vehicles. For example, vehicles that have beenmodified to have a distinctive shape may be uniquely identified by alaser signature.

Another example of a vehicle identifier is a vehicle signature generatedusing a magnetic scan (hereinafter referred to as an inductivesignature). The inductive signature of a vehicle is a parameter thatreflects the metal distribution across the vehicle and, therefore, maybe used to classify the vehicle and, in some circumstances, to uniquelyidentify the vehicle (e.g., if the metal distribution of a particularvehicle is unique to that vehicle because of unique modifications tothat vehicle). The inductive signature may include information that maybe used to determine one or more of the axle count (and likely thenumber of tires) of the vehicle, the type of engine used in the vehicle,and the type or class of vehicle. In one implementation, the imageacquisition module 624 includes a pair of vehicle detection loops, anaxle detection loop, and a camera trigger loop in each lane.

Once the two or more vehicle identifiers are extracted by the imageprocessing module 625, the image processing module 625 stores theextracted vehicle identifiers in the extracted vehicle identifierdatabase 6181. Ideally, the computer 612 would then be able to uniquelyidentify the owner of the vehicle by choosing a vehicle identifier thatuniquely identifies the vehicle (e.g., license plate information orvehicle fingerprint) and searching one or more internal or externalvehicle record databases for a record containing a matching vehicleidentifier. Unfortunately, extracting a vehicle identifier is animperfect process. The extracted vehicle identifier may not correspondto the actual vehicle identifier, and therefore, may not uniquelyidentify the vehicle. An incorrectly or partially extracted vehicleidentifier may not match the vehicle identifier of any vehicle, maymatch the vehicle identifier of the wrong vehicle, or may match thevehicle identifiers of more than one vehicle. To increase identificationaccuracy, the computer 612 of the system 600 implements a multi-tieridentification process using two or more vehicle identifiers.

FIG. 7 is a flow chart of an exemplary two-tier identification process700 that may be implemented to increase the accuracy of vehicleidentification. Image and/or sensor data is captured for a vehicle thatinteracts with a toll facility (hereinafter referred to as the “targetvehicle”) and two vehicle identifiers are extracted from the captureddata (block 710). In one implementation, only image data is collectedand the two vehicle identifiers extracted are a license plate number anda vehicle fingerprint. In another implementation, image data andinductive sensor data are collected and the vehicle identifiersextracted are the vehicle fingerprint and the inductive signature.

One of the two extracted vehicle identifiers is designated as the firstvehicle identifier and used to identify a set of one or more matchingvehicle candidates (block 720). Typically, the vehicle identifier thatis deemed to be the least able to accurately and/or uniquely identifythe target vehicle is designated as the first vehicle identifier. Forexample, if the two extracted vehicle identifiers were license platenumber and vehicle fingerprint, the license plate number would bedesignated as the first vehicle identifier because of the lower expectedaccuracy of vehicle identification through license plate matching ascompared to fingerprint matching. The one or more matching vehiclecandidates may be determined, for example, by accessing a vehicle recorddatabase and finding records that contain vehicle identifiers that matchor nearly match the first vehicle identifier.

Once the set of one or more matching vehicle candidates is determined,the target vehicle is identified from the set based on the secondvehicle identifier (block 730). For example, if 12 vehicle candidateswere identified as matching a partially extracted license plate number,the target vehicle is identified by accessing the vehicle fingerprintsfor each of the 12 vehicle candidates and determining which of the 12vehicle fingerprints matches the extracted vehicle fingerprint. If nomatch is found within a predetermined confidence threshold, manualidentification of the vehicle may be used. In another implementation,one or more larger sets (e.g., supersets) of matching vehicle candidatesare determined successively or concurrently by changing (e.g.,loosening) the criteria for matching and additional attempts are made toidentify the target vehicle from each of the one or more larger setsprior to resorting to manual identification.

In some implementations, the toll management system may be purposefullydesigned to identify a larger set of matching vehicle candidates duringoperation 720 to, for example, ensure that the expected lesser accuracyof vehicle identification through the first identifier does noterroneously result in exclusion of the target vehicle from the set ofmatching vehicle candidates. For example, if the first vehicleidentifier is a license plate number, the license plate readingalgorithm may be intentionally modified in, for example, two ways: (1)the matching criteria of the license plate reading algorithm may beloosened to enable the algorithm to generate a larger set of matchingvehicle candidates and (2) the license plate reading algorithm may be“detuned” by lowering the read confidence threshold used to determinewhether a read result is included in the matching candidate set. Forinstance, the license plate reading algorithm may be loosened to onlyrequire a matching vehicle candidate to match a subset or lesser numberof the characters in the license plate number extracted for the targetvehicle. Additionally or alternatively, the read confidence thresholdmay be lowered to enable previously suspected incorrect reads (i.e.,partial or low confidence reads) to be included in the matching vehiclecandidate set.

The two-tier identification process 700 provides greater identificationaccuracy over a single-tier/single identifier identification system byrequiring that two vehicle identifiers be successfully matched forsuccessful vehicle identification. Moreover, the process 700 may providegreater identification speed by limiting the matching of the secondvehicle identifier to only those vehicle candidates having records thatsuccessfully match the first vehicle identifier. This can provideincreased speed if, for example, the extracted second vehicle identifieris time-consuming to match against other such identifiers or if a largenumber of other such identifiers exists (e.g., millions of identifiersfor millions of vehicles in a vehicle database).

In another implementation, two or more second identifiers are used toidentify the target vehicle from among the set of matching vehiclecandidates. Each of the second identifiers must match the same candidatevehicle to within a predetermined confidence level for successfulvehicle identification. Alternatively, the degree of matching of each ofthe two or more second identifiers may be weighted and a combinedequivalent matching score may be generated. If the combined equivalentmatching score is above a predetermined threshold, the identification isdeemed successful.

In one implementation, each second vehicle identifier is assigned amatch confidence level number that ranges from 1 to 10, where 1corresponds to no match and 10 corresponds to an exact match. Eachvehicle identifier is also assigned a weight value from 1 to 10, withgreater weight values being assigned to vehicle identifiers that areconsidered more accurate in uniquely identifying vehicles. If, forexample, the second vehicle identifiers are a laser signature andlicense plate information, a weighting of 6 may be assigned to the lasersignature and a greater weighting of 9 may be assigned to the licenseplate information. If a combined equivalent matching score of 100 isnecessary for an identification to be deemed successful and the licenseplate information matches to a confidence level of 7 and the lasersignature also matches to a confidence level of 7, the combinedequivalent matching score would be 7*6+7*9=105 and the identificationwould be considered successful.

In another implementation, two or more first vehicle identifiers areused to identify vehicles in the set of matching vehicle candidates.Each of the first vehicle identifiers for a possible candidate vehiclemust match the target vehicle to within a predetermined confidence levelfor the possible candidate vehicle to be included in the set of matchingvehicle candidates. Alternatively, the degree of matching of each of thetwo or more first identifiers may be weighted and a combined equivalentmatching score may be generated. If the combined equivalent matchingscore is above a predetermined threshold, the possible candidate vehicleis included in the set of matching vehicle candidates.

In another implementation, the second identifier is not used to uniquelyidentify the target vehicle from among the vehicles in the set ofmatching vehicle candidates. Rather, the second identifier is used togenerate a new and smaller set of matching vehicle candidates as asubset of the set determined using the first identifier, and a thirdidentifier is then used to uniquely identify the target vehicle fromthis subset of matching vehicle candidates. In yet anotherimplementation, multiple vehicle identifiers are used to successivelyreduce the set of matching vehicle candidates and the target vehicle isuniquely identified from the successively reduced subset through use ofone or more final vehicle identifiers. In yet another implementation,each of the multiple vehicle identifiers is used to generate its own setof matching vehicle candidates through matching and near matchingtechniques and the reduced set is the intersection of all of thedetermined sets. In yet another implementation, the reduced set isdetermined using a combination of the above-described techniques.

FIG. 8 is a flow chart of an exemplary two-tier identification process800 that may be implemented to increase the accuracy and/or automationof vehicle identification. Process 800 is an implementation of process700 wherein the first identifier is a license plate number and thesecond identifier is a vehicle fingerprint. In particular, process 800includes operations 810-830, and associated sub-operations, thatcorrespond to and illustrate one possible implementation of operations710-730, respectively. For convenience, particular components describedwith respect to FIG. 6 are referenced as performing the process 800.However, similar methodologies may be applied in other implementationswhere different components are used to define the structure of thesystem, or where the functionality is distributed differently among thecomponents shown by FIG. 6.

The image acquisition module 624 captures image data for the targetvehicle based on an interaction between the target vehicle and the tollfacility 628 (block 812). In another implementation, the imageacquisition module 624 additionally or alternatively captures sensordata including, for example, laser scanning and/or loop sensor data. Theimage processing module 625 obtains license plate data, including, forexample, a complete or partial license plate number and state, for thetarget vehicle from the captured image data (block 814). Optionally, theimage processing module 625 also may determine a vehicle fingerprint forthe target vehicle from the image data. In another implementation, theimage processing module 625 may determine other vehicle signature data,such as, for example, laser and/or inductive signature data, from theimage data and/or sensor data.

The computer 612 stores the captured image data in the image database614 and stores the extracted license plate data in the extractedidentifier database 6181. If applicable, the toll management computer612 also stores the extracted vehicle fingerprint and other signaturedata, such as, for example, the inductive signature and/or lasersignature, in the extracted identifier database 6181.

The computer 612 accesses a set of vehicle identification records fromthe vehicle record database 6182 (block 822). Each of the vehicleidentification records associates an owner/driver of a vehicle withvehicle identifier data. The computer 612 compares the extracted licenseplate data with the license plate data in the set of vehicleidentification records (block 824) and identifies a set of candidatevehicles from the vehicles having records in the set of records (block826). The comparison may be done using matching or near matchingtechniques.

The computer 612 accesses extracted vehicle fingerprint data for thetarget vehicle (block 832). If the vehicle fingerprint has not alreadybeen determined/extracted from the captured image data, the computer 612calculates the vehicle fingerprint and stores the vehicle fingerprint inthe extracted vehicle identifier database 6181.

The computer 612 accesses vehicle fingerprint data for a vehicle in theset of candidate vehicles by accessing the corresponding vehicleidentification record (block 834) and compares the vehicle fingerprintdata for the target vehicle to the vehicle fingerprint data for thecandidate vehicle (block 836). The computer 612 identifies the candidatevehicle as the target vehicle based on the results of the comparison ofthe vehicle fingerprint data (block 838). If the vehicle fingerprintdata matches within a predetermined confidence threshold, the candidatevehicle is deemed to be the target vehicle, and the owner/driver of thecandidate vehicle is deemed to be the owner/driver of the targetvehicle.

FIGS. 9A-9C are a flow chart of an exemplary two-tier identificationprocess 900 that may be implemented to increase the accuracy of vehicleidentification while minimizing the need for manual identification ofvehicles. Process 900 is another implementation of process 700 whereinthe first identifier is a license plate number and the second identifieris a vehicle fingerprint. In particular, process 900 includes operations910-930, and associated sub-operations, that correspond to andillustrate one possible implementation of operations 710-730,respectively. For convenience, particular components described withrespect to FIG. 6 are referenced as performing the process 800. However,similar methodologies may be applied in other implementations wheredifferent components are used to define the structure of the system, orwhere the functionality is distributed differently among the componentsshown by FIG. 6.

The image acquisition module 624 captures image and sensor data for thetarget vehicle (block 911). Roadside sensors, for example, triggercameras that capture front and rear images of the target vehicle. Othersensors may capture additional data used forclassification/identification of the vehicle. For example, a laser scanmay be used to determine laser signature data including the height,width, length, axle count, and vehicle dimensional profile. Sensors alsomay be used to determine data related to the transaction between thetarget vehicle and the toll facility 628 such as, for example, theweight of the vehicle, the speed of the vehicle, and transponder dataassociated with the vehicle.

The image processing module 625 performs a license plate read on thecaptured image data, creates a vehicle fingerprint from the capturedimage data, and optionally determines other vehiclesignature/classification data from the captured sensor data (block 912).For example, the image processing module 625 may use an automatedlicense plate read algorithm to read one or more of the captured images.The license plate read algorithm may read the captured images, forexample, in a prioritized order based on visibility of the plate and itslocation in the image. The license plate read results may include one ormore of a license plate number, a license plate state, a license platestyle, a read confidence score, a plate location in the image, and aplate size. The image processing module 625 also may apply a visualsignature extraction algorithm to generate the vehicle fingerprint forthe target vehicle. The visual signature extraction algorithm may besimilar to that developed by JAI-PULNiX Inc. of San Jose, Calif. anddescribed in U.S. Pat. No. 6,747,687. The computer 612 stores thecaptured images in the image database 614 and stores the license plateread results, vehicle fingerprint, and other vehiclesignature/classification data in the extracted vehicle identifierdatabase 6181.

The image processing module 625 determines whether the captured imageshave provided any partial or complete read results for the license platenumber and state of the target vehicle (block 913). If no partial orcomplete read results were provided by the captured images, process 900proceeds to operation 941 of the manual identification process 940.

If partial or complete read results for the license plate number andstate of the target vehicle were provided by the captured images,computer 612 searches the vehicle record database 6182 and read errorsdatabase 6183 for the exact (either partial or complete) license platenumber (as read by the license plate reader) (block 921).

The vehicle record database 6182 includes records for all vehiclespreviously recognized and potentially includes records for vehicles thatare anticipated to be seen. The vehicle record database 6182 istypically populated through a registration process during which adriver/owner of a vehicle signs the vehicle up for automated tollpayment handling. The driver/owner of a vehicle may sign a vehicle upfor automated toll payment handling by driving the vehicle through aspecial registration lane in the toll facility 628 and providing acustomer service representative at the facility 628 with his or heridentity and other contact information. The image acquisition module 624and the image processing module 625 capture the license plate number,the fingerprint, and other identification/classification data (e.g., thevehicle dimensions) of the user's vehicle while the vehicle traversesthe facility 628. The vehicle and owner identification data is stored ina new vehicle identification record associated with the newly registeredvehicle and owner/driver.

Alternatively, a driver/owner may register a vehicle for automatic tollpayment handling by simply driving through the facility 628, withoutstopping. The computer 612 captures image data and sensor data for thevehicle and attempts to identify the driver/owner by reading the licenseplate image and looking up the read results in a database of an externalsystem 634 (e.g., vehicle registration authorities). If an owner/driveris identified, the computer 612 bills the owner/driver. Once a billingrelationship has been successfully setup, the computer 612 officiallyregisters the vehicle, generates as necessary the vehicle fingerprintdata and other signature/classification data from the captured image andsensor data, and stores these in a vehicle identification recordassociated with the identified owner/driver.

In another implementation, the computer 612 is configured to obtaingreater accuracy in identifying an unregistered driver/owner by lookingup the license plate read results in a database of a vehicleregistration authority (or other external system) and requesting acorresponding vehicle identification number (VIN) from the vehicleregistration authority (or other external system). The computer 612 usesthe VIN to determine the make, model, and year of the vehicle. The make,model, and year of the vehicle may be used to determine the length,width, and height of the vehicle. The computer 612 may then determine asuccessful match of the target vehicle with a vehicle registered withthe vehicle registration authority not only by comparing license platedata but also by comparing vehicle dimensions (as captured, for example,in a laser signature and/or an inductive signature). Typically, thecomputer 612 will consider a match successful if the license plate readresults for the target vehicle match the license plate data for thevehicle registered with the vehicle registration authority to within apredetermined threshold and the vehicle dimensions of both vehiclesmatch within a given tolerance.

The make, model, and year of a vehicle may be used, for example, todetermine the length, width, and height of the vehicle by eitheraccessing this information from a public database or from a 3^(rd) partydatabase or, additionally or alternatively, by accessing the vehiclerecords database 6182 to retrieve the length, width, and height datafrom one or more vehicle identification records corresponding tovehicles having the same make, model, and year as the target vehicle.Given that a vehicle's dimensions may change if the vehicle has beenmodified, the length, width, and height accessed from the vehicleidentification records may vary by vehicle. Accordingly, the computer612 may need to statistically determine the appropriate dimensions forcomparison by, for example, taking the average or median length, width,and height dimensions.

In one implementation, the computer 612 identifies a vehicle in partthrough use of an electronic signature that includes a laser signatureand/or an inductive (i.e., magnetic) signature. When a vehicle transactswith the toll system, an electronic signature is captured for thevehicle. The image and measurements of the vehicle created by the laser(i.e., the laser signature) and/or the magnetic scan (i.e., theinductive signature) are compared against known dimensions and images ofvehicles based on vehicle identification number (VIN) that were, forexample, previously captured by the toll system or by an externalsystem. By comparing the electronic signature image and dimensions toknown dimensions of vehicles based on VIN, the search for a matchingvehicle and associated VIN may be narrowed. If, for example, an LPR forthe vehicle has a low confidence level, but the electronic signature ofthe vehicle has been captured, the toll system may access a database, asdescribed above, of known dimensions and images for vehicles andassociated VINs and cross reference the electronic signature dimensionsand images against the database to identify the matching vehicle VIN oridentify potential matching vehicle candidates/VINs. The read errorsdatabase 6183 links previous incorrect read results to correct vehicleidentification records. For example, when automated vehicleidentification fails but manual vehicle identification succeeds, thecaptured vehicle identification data (e.g., the license plate readresult) that led to an “error” (i.e., an identification failure) by theautomated system is stored in an error record in the read errorsdatabase 6183 that is linked to the vehicle identification record thatwas manually identified for the vehicle. Thus, when the same vehicleidentification data is captured again at a later date, the computer 612may successfully identify the vehicle automatically by accessing theerror record in the read errors database 6183, which identifies thecorrect vehicle identification record for the vehicle, without requiringanother manual identification of the vehicle.

An error record also may be generated and stored in the read errorsdatabase 6183 when automated identification of the vehicle succeedsbased on a near match of an incorrect license plate read result. Forexample, if the license plate number “ABC123” is read as “ABC128” andthe identified candidate match set is “ABC128,” “ABC123,” “ABG128” and“ABC128” which in turn yields the correct match of “ABC123,” an errorrecord may be created that automatically links a license plate readresult of “ABC128” to the vehicle having the license plate number“ABC123.”

The computer 612 determines whether any vehicle identification recordscorrespond to the license plate read results for the target vehicle(block 922). If no vehicle identification records correspond to the readresults, the computer 612 performs an extended search (block 923).

The computer 612 performs an extended search by changing or looseningthe criteria for a successful match or detuning the license plate readalgorithm. For example, the computer 612 may perform an extended searchby one or more of the following: (1) comparing a subset of the licenseplate number read result with the characters of the license platenumbers stored in the vehicle record database 6182 (e.g., the last twocharacters of the license plate number may be omitted such that if thelicense plate number is “ABC123,” any vehicles having license platenumbers “ABC1**” are deemed matching candidates, wherein “*” is avariable); (2) comparing a subset of the license plate number readresult in reverse order with the characters of the license plate numbersstored in the vehicle record database 6182 in reverse order (e.g., thelast two characters of the license plate number in reverse order may beomitted such that if the license plate number is “ABC123”, which is“321CBA” in reverse order, any vehicles having license plate numbers inreverse order of “321C**” are deemed matching candidates, wherein “*” isa variable); and (3) other near match techniques including comparingmodified versions of the license plate read result and license platenumbers stored in the vehicle record database 6182 in which some ofeither or both are substituted and/or removed to reduce the impact ofmisread characters. For example, if the OCR algorithm does not indicatea confidence level above a predetermined threshold in a read result of acharacter on the license plate, that character may be ignored.Additionally or alternatively, if the OCR algorithm indicates that acharacter on the license plate may be one of two possible differentcharacters, both alternative characters may be used in the extendedsearch.

The computer 612 determines whether any vehicle identification recordscorrespond to the read results for the target vehicle after performingthe extended search (block 924). If no vehicle identification recordsare found, process 900 proceeds to operation 941 of the manualidentification process 940 (block 924).

Referring to FIG. 9B, if either the search or the extended search leadto identification of one or more vehicle identification records, thecomputer 612 retrieves vehicle fingerprint and optionally other vehiclesignature/classification data from the identified vehicle identificationrecords (block 931). The computer 612 compares the retrieved vehiclefingerprint and optionally other vehicle signature/classification datafor each matching vehicle candidate with the corresponding dataassociated with the target vehicle to identify one or more possiblematches (block 932). The vehicle fingerprint comparison may be performedwith a comparison algorithm identical or similar to the one developed byJAI-PULNiX Inc. of San Jose, Calif. and described in U.S. Pat. No.6,747,687.

A possible match may be defined, for example, as a vehicle fingerprintmatch with a confidence score greater than or equal to a predefinedthreshold and all or some of the other classification/signature datafalling within tolerances defined for each data type. For example, ifthe fingerprint matching algorithm generates a score of 1 to 1000, where1 is no match and 1000 is a perfect match, then a score greater than orequal to 900 may be required for a successful match. Additionally, ifthe other classification/signature data includes target vehicle height,width, and length, then the height, width, and length of the vehiclecandidate may be required to be within plus or minus four inches of theextracted height, width, and length of the target vehicle for asuccessful match. One or more vehicle identification records may bedeemed to correspond to vehicles that possibly match the target vehicle.

The computer 612 determines whether a possible match is sufficient toautomatically identify the vehicle without human intervention bydetermining a combined equivalent matching score for each possible matchand comparing the result to a predetermined automated confidencethreshold (block 933). The computer 612 may, for example, determine acombined equivalent matching score for each possible match in a mannersimilar to that described previously with respect to process 700.Specifically, the computer 612 may assign a match confidence levelnumber to the fingerprint matching and, optionally, to theclassification/signature data matching, assign a weight to each datatype, and calculate a combined equivalent matching score by combiningthe weighted match confidence level numbers. If the combined equivalentmatching score exceeds a predetermined automated confidence threshold,the computer 612 deems the target vehicle successfully identified andprocess 900 proceeds to operation 937 for recording the transactionevent between the identified vehicle and the facility 628. If more thanone possible match exceeds the automated confidence threshold, theautomated identification process may be faulty, and process 900 mayoptionally proceed (not shown) to operation 941 of the manualidentification process 940.

If no possible match is deemed sufficient to automatically identify thevehicle without human intervention, the computer 612 determines whetherone or more possible matches satisfy a lower probable match threshold(block 934). The computer 612 may, for example, determine that apossible match satisfies the probable match threshold if the combinedequivalent matching score of the possible match is higher than theprobable match threshold but lower than the automated confidencethreshold.

If at least one possible match satisfies the probable match threshold,the computer 612 enables an operator to perform visual match truthing(block 935). Visual match truthing is a process in which the computer612 presents one or more of the images of the target vehicle to theoperator along with one or more of the reference images associated withthe vehicle or vehicles that probably match the target vehicle. Theoperator quickly confirms or rejects each probable match with a simpleyes or no indication by, for example, selecting the appropriate buttonson a user interface (block 936). The operator also may optionallyprovide a detailed explanation to support his or her response.

If the match exceeds the automated confidence threshold or is visuallyconfirmed by the operator through visual match truthing, the computer612 creates a record of the event (i.e., a record of the interactionbetween the positively identified target vehicle and the facility 628)as, for example, a billable or non-revenue transaction (block 937). Ifthe match was confirmed through visual match truthing, the computer 612may optionally update the read errors database 6183 to include theextracted vehicle identification data and a link that associates theextracted vehicle identification data with the correct vehicleidentification record (block 938).

Referring also to FIG. 9C, the computer 612 is configured to enable anoperator to manually identify the target vehicle (block 941) under thefollowing circumstances: (1) the captured images of the target vehicledo not provide any partial or complete read results for the licenseplate number and state of the target vehicle (block 913); (2) no vehicleidentification records are found that correspond to the license plateread results for the target vehicle after performing an extended search(block 924); (3) one or more possible matches are found but theconfidence level in the one or more possible matches, as reflected bycombined equivalent matching scores, fall below both the automatedconfidence threshold and the probable match threshold (block 934); and(4) one or more probable matches are found but a human operator rejectsthe one or more probable matches through visual match truthing (block936).

The human operator attempts to manually identify the vehicle by (1)reading the license plate(s), and (2) observing vehicle details capturedby the image acquisition module 624, and (3) comparing the license platedata and vehicle details with data available from the vehicle recordsdatabase 6182, read errors database 6183, and/or databases of externalsystems 634. License plates read by a human operator may be confirmed bycomparison with automated license plate reader results and/or multipleentry by multiple human operators.

The manual identification may be deemed successful if the manuallycollected data, weighed against definable criteria for a positivevehicle match, exceeds a predetermined identification confidencethreshold (block 942). This determination may be done by the computer612, the operator that provided the manual data, and/or a more qualifiedoperator.

In one implementation, if a vehicle cannot be positively identifiedautomatically and no near matches are found, one or more images of thevehicle are displayed to a first human reviewer. The first humanreviewer inspects the images and manually specifies the license platenumber that the first reviewer believes corresponds to the vehicle basedon the images. Because this manual review by the first human reviewer isalso subject to error (e.g., perceptual or typographical error), thelicense plate read by the first human reviewer is compared to an LPRdatabase to determine whether the license plate number specified by thefirst human reviewer exists. Additionally, if a database record havingfingerprint data corresponding to the license plate read exists, afingerprint comparison also may be performed. If the first humanreviewer read result does not match any known LPR result or vehicle, theone or more images of the vehicle may be displayed to a second humanreviewer. The second human reviewer inspects the images and manuallyspecifies the license plate number that the second human reviewerbelieves corresponds to the vehicle based on the images. If the readresult by the second human reviewer is different than the read result bythe first human reviewer, a read by a third human reviewer, who istypically a more qualified reviewer, may be necessary. In sum, the firsthuman reviewer read is effectively a jumping off point to re-attempt anautomated match. If the automated match still fails, multiple humanreviewers must show agreement in reading the license plate for the readto be deemed accurate.

If the vehicle is not successfully identified, the computer 612 createsa record of the event as an unidentified or unassigned transaction(block 943). If the vehicle is successfully identified, the computer 612creates a record of the event as, for example, a billable or non-revenuetransaction (block 937). If the vehicle had never been previouslyidentified, the computer 612 may create a new vehicle identificationrecord for the vehicle and its owner/driver in the vehicle recorddatabase 6182. The computer 612 also may update the read errors database6183 to include the extracted vehicle identification data and a linkthat associates the extracted vehicle identification data with thecorrect vehicle identification record (block 938).

The above applications represent illustrative examples and the disclosedtechniques disclosed can be employed in other applications. Further, thevarious aspects and disclosed techniques (including systems andprocesses) can be modified, combined in whole or in part with eachother, supplemented, or deleted to produce additional implementations.

The systems and techniques described here can be implemented in digitalelectronic circuitry, or in computer hardware, firmware, software, or incombinations of them. The systems and techniques described here can beimplemented as a computer program product, i.e., a computer programtangibly embodied in an information carrier, e.g., in a machine-readablestorage device or in a propagated signal, for execution by, or tocontrol the operation of, data processing apparatus, e.g., aprogrammable processor, a computer, or multiple computers. A computerprogram can be written in any form of programming language, includingcompiled or interpreted languages, and it can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment. Acomputer program can be deployed to be executed on one computer or onmultiple computers at one site or distributed across multiple sites andinterconnected by a communication network.

Method steps of the systems and techniques described here can beperformed by one or more programmable processors executing a computerprogram to perform functions of the invention by operating on input dataand generating output. Method steps can also be performed by, andapparatus of the invention can be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The typical elements of a computer are aprocessor for executing instructions and one or more memory devices forstoring instructions and data. Generally, a computer will also include,or be operatively coupled to receive data from or transfer data to, orboth, one or more mass storage devices for storing data, e.g., magnetic,magneto-optical disks, or optical disks. Information carriers suitablefor embodying computer program instructions and data include all formsof non-volatile memory, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic diskssuch as internal hard disks and removable disks; magneto-optical disks;and CD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in special purpose logic circuitry.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display devicesuch as a CRT (cathode ray tube) or LCD (liquid crystal display) monitorfor displaying information to the user and a keyboard and a pointingdevice such as a mouse or a trackball by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback, such as visual feedback,auditory feedback, or tactile feedback; and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back-end component, e.g., as a dataserver, or that includes a middleware component, e.g., an applicationserver, or that includes a front-end component, e.g., a client computerhaving a graphical user interface or an Web browser through which a usercan interact with an implementation of the invention, or any combinationof such back-end, middleware, or front-end components. The components ofthe system can be interconnected by any form or medium of digital datacommunication, e.g., a communication network. Examples of communicationnetworks include a local area network (“LAN”), a wide area network(“WAN”), and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Other implementations are within the scope of the following claims.

What is claimed is:
 1. A computer-implemented method of identifying avehicle in a toll system, the method comprising: accessing image datafor a vehicle transacting with a toll system; obtaining first vehicleidentifier data from the accessed image data for the transactingvehicle; accessing a set of records that includes first vehicleidentifier data for vehicles; executing, using at least one processingdevice, an algorithm to: compare the first vehicle identifier data forthe transacting vehicle with the first vehicle identifier data forvehicles in the set of records, and identify a set of vehicle candidatesfrom the vehicles having records in the set of records, wherein theidentified set of vehicle candidates excludes at least one vehiclehaving a record in the set of records; and selecting, from the set ofvehicle candidates, a vehicle candidate as corresponding to thetransacting vehicle by: accessing second vehicle identifier data for thetransacting vehicle, the second vehicle identifier data being data foridentifying a vehicle that is distinct from first vehicle identifierdata, accessing second vehicle identifier data for a vehicle candidatein the set of vehicle candidates, comparing, using the at least oneprocessing device, the second vehicle identifier data for thetransacting vehicle with the second vehicle identifier data for thevehicle candidate in the set of vehicle candidates, and identifying thevehicle candidate in the set of vehicle candidates as the transactingvehicle based on results of the comparison of second vehicle identifierdata, wherein executing the algorithm to compare the first vehicleidentifier data for the transacting vehicle with the first vehicleidentifier data for vehicles in the set of records includes: searching avehicle record database for records that include first vehicleidentifier data that exactly match the first vehicle identifier dataobtained for the transacting vehicle, and performing an extended searchof the vehicle record database for records that include first vehicleidentifier data that nearly match the first vehicle identifier dataobtained for the transacting vehicle, the extended search beingconditioned on no vehicle identification records being found thatinclude first vehicle identifier data that exactly match the firstvehicle identifier data obtained for the transacting vehicle.
 2. Themethod of claim 1, wherein comparing the first vehicle identifier datafor the transacting vehicle with the first vehicle identifier data forvehicles in the set of records includes comparing the first vehicleidentifier data using predetermined matching criteria, and furthercomprising changing the predetermined matching criteria to increase thenumber of vehicles in the identified set of vehicles.
 3. The method ofclaim 2, wherein changing the predetermined matching criteria toincrease the number of vehicles in the identified set of vehicles isconditioned on a failure to identify any vehicles in the set of vehiclesas the transacting vehicle based on results of the comparison of secondvehicle identifier data.
 4. The method of claim 1, further comprisingaccessing laser signature data, wherein the laser signature datacomprises data obtained by using a laser to scan the transactingvehicle.
 5. The method of claim 4, wherein the laser signature dataincludes one or more of an overhead electronic profile of thetransacting vehicle, an axle count of the transacting vehicle, and a 3Dimage of the transacting vehicle.
 6. The method of claim 4, furthercomprising comparing laser signature data for the transacting vehiclewith laser signature data for vehicles in the set of records, andwherein identifying a set of vehicles from the vehicles having recordsin the set of records includes identifying the set of vehicles based onthe results of the comparison of the first vehicle identifier data andthe results of the comparison of the laser signature data.
 7. The methodof claim 1, further comprising accessing inductive signature data,wherein the inductive signature data comprises data obtained through useof a loop array over which the transacting vehicle passes.
 8. The methodof claim 7, wherein the inductive signature data includes one or more ofan axle count of the transacting vehicle, a type of engine of thetransacting vehicle, and a vehicle type or class for the transactingvehicle.
 9. The method of claim 7, further comprising comparing lasersignature data for the transacting vehicle with laser signature data forvehicles in the set of records, and wherein identifying a set ofvehicles from the vehicles having records in the set of records includesidentifying the set of vehicles based on the results of the comparisonof the first vehicle identifier data and the results of the comparisonof the inductive signature data.
 10. The method of claim 1, whereinidentifying the vehicle candidate in the set of vehicle candidates asthe transacting vehicle includes identifying the vehicle candidate asthe transacting vehicle if the comparison of the second vehicleidentifier data for the transacting vehicle with the second vehicleidentifier data for the vehicle candidate in the set of vehiclecandidates indicates a match having a confidence level that exceeds aconfidence threshold.
 11. The method of claim 10, wherein identifyingthe vehicle candidate in the set of vehicle candidates as thetransacting vehicle includes identifying the vehicle candidate in theset of vehicle candidates as the transacting vehicle without humanintervention if the confidence level of the match exceeds a firstconfidence threshold.
 12. The method of claim 11, wherein identifyingthe vehicle candidate in the set of vehicle candidates as thetransacting vehicle includes identifying the vehicle candidate in theset of vehicle candidates as the transacting vehicle if the confidencelevel of the match is less than the first confidence threshold butgreater than a second confidence threshold and a human operator confirmsthe match.
 13. The method of claim 12, further comprising enabling thehuman operator to confirm or reject the match by: enabling the humanoperator to perceive the accessed image data for the transactingvehicle, enabling the human operator to perceive one or more referenceimages associated with the vehicle candidate, and enabling the humanoperator to interact with a user interface to indicate rejection orconfirmation of the match.
 14. The method of claim 12, whereinidentifying the vehicle candidate in the set of vehicle candidates asthe transacting vehicle includes identifying the vehicle candidate asthe transacting vehicle if the confidence level of the match is lessthan the first and second confidence thresholds and a human operatormanually identifies the vehicle candidate as the transacting vehicle byaccessing the image data for the transacting vehicle and the record forthe vehicle candidate in the set of records.
 15. The method of claim 1,wherein identifying the vehicle candidate in the set of vehiclecandidates as the transacting vehicle includes identifying the vehiclecandidate based on vehicle identification number (VIN), laser signature,inductive signature, and image data.
 16. The method of claim 1, whereinthe second vehicle identifier comprises vehicle fingerprint data for thetransacting vehicle, the vehicle fingerprint data for the transactingvehicle being based on the accessed image data for the transactingvehicle.
 17. An apparatus for identifying a vehicle in a toll system,the apparatus comprising: an image capture device configured to captureimage data for a vehicle transacting with a toll system; and one or moreprocessing devices communicatively coupled to each other and to theimage capture device and configured to: access the image data for thetransacting vehicle; obtain first vehicle identifier data from theaccessed image data for the transacting vehicle; access a set of recordsthat includes first vehicle identifier data for vehicles; execute analgorithm to: compare the first vehicle identifier data for thetransacting vehicle with the first vehicle identifier data for vehiclesin the set of records, and identify a set of vehicle candidates from thevehicles having records in the set of records, wherein the identifiedset of vehicle candidates excludes at least one vehicle having a recordin the set of records; and select, from the set of vehicle candidates, avehicle candidate as corresponding to the transacting vehicle by:accessing second vehicle identifier data for the transacting vehicle,the second vehicle identifier data being data for identifying a vehiclethat is distinct from first vehicle identifier data, accessing secondvehicle identifier data for a vehicle candidate in the set of vehiclecandidates, comparing the second vehicle identifier data for thetransacting vehicle with the second vehicle identifier data for thevehicle candidate in the set of vehicle candidates, and identifying thevehicle candidate in the set of vehicle candidates as the transactingvehicle based on results of the comparison of second vehicle identifierdata, wherein the one or more processing devices being configured toexecute an algorithm to compare the first vehicle identifier data forthe transacting vehicle with the first vehicle identifier data forvehicles in the set of records comprises the one or more processingdevices being configured to: search a vehicle record database forrecords that include first vehicle identifier data that exactly matchthe first vehicle identifier data obtained for the transacting vehicle,and perform an extended search of the vehicle record database forrecords that include first vehicle identifier data that nearly match thefirst vehicle identifier data obtained for the transacting vehicle, theextended search being conditioned on no vehicle identification recordsbeing found that include first vehicle identifier data that exactlymatch the first vehicle identifier data obtained for the transactingvehicle.
 18. A computer-readable storage device storing softwarecomprising instructions executable by one or more computers which, uponsuch execution, cause the one or more computers to perform operationscomprising: accessing image data captured by an image capture device,the image data corresponding to a vehicle transacting with a tollsystem; obtaining first vehicle identifier data from the accessed imagedata for the transacting vehicle; accessing a set of records thatincludes first vehicle identifier data for vehicles; executing analgorithm to: compare the first vehicle identifier data for thetransacting vehicle with the first vehicle identifier data for vehiclesin the set of records, and identify a set of vehicle candidates from thevehicles having records in the set of records, wherein the identifiedset of vehicle candidates excludes at least one vehicle having a recordin the set of records; and selecting, from the set of vehiclecandidates, a vehicle candidate as corresponding to the transactingvehicle by: accessing second vehicle identifier data for the transactingvehicle, the second vehicle identifier data being data for identifying avehicle that is distinct from first vehicle identifier data, accessingsecond vehicle identifier data for a vehicle candidate in the set ofvehicle candidates, comparing the second vehicle identifier data for thetransacting vehicle with the second vehicle identifier data for thevehicle candidate in the set of vehicle candidates, and identifying thevehicle candidate in the set of vehicle candidates as the transactingvehicle based on results of the comparison of second vehicle identifierdata, wherein executing the algorithm to compare the first vehicleidentifier data for the transacting vehicle with the first vehicleidentifier data for vehicles in the set of records includes: searching avehicle record database for records that include first vehicleidentifier data that exactly match the first vehicle identifier dataobtained for the transacting vehicle, and performing an extended searchof the vehicle record database for records that include first vehicleidentifier data that nearly match the first vehicle identifier dataobtained for the transacting vehicle, the extended search beingconditioned on no vehicle identification records being found thatinclude first vehicle identifier data that exactly match the firstvehicle identifier data obtained for the transacting vehicle.